Title: Automatic detection and diagnosis of cocoa diseases using mobile tech and deep learning

Authors: Richard K. Lomotey; Sandra Kumi; Rita Orji; Ralph Deters

Addresses: The Pennsylvania State University – Beaver, Information Sciences and Technology, Monaca, PA, USA ' Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada ' Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada ' Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada

Abstract: Cocoa is a cash crop that contributes about 3% to the gross domestic product (GDP) of Ghana's economy and makes up about 20% of total export receipts according to the Ghana statistical service. However, revenue has been hampered recently by the outbreak of cocoa diseases such as Swollen shoot and black pod thereby causing up to 11% loss of the crop. There is, therefore, a need for urgent intervention by all stakeholders within the cocoa production sector. In this research, we aim to employ mobile technology and machine learning (ML) techniques to enhance the early detection and diagnosis of the two major diseases that affect cocoa production namely - swollen shoot and black pod. Specifically, a distributed mobile application is developed that enables farmers to take a picture or video of the cocoa and the app will analyse and automatically detect the specific disease. The app consequently suggests the best treatment to undertake using an inbuilt-information guide. The automatic detection and diagnosis of diseases are based on deep convolutional neural networks (CNN) for image analysis, classification, and detection. The research analysed 2,828 cocoa images spread across three class labels. We built and trained four CNN models, namely CentreNet ResNet50 V2, EfficientDet D0, SSD MobileNet V2, and SSD ResNet50 V1 FPN. We found the best generalised and fastest model to be the SSD MobileNet V2 with a detection confidence score of approximately 88.0%.

Keywords: agriculture; mobile; deep learning; cocoa production; machine learning; ML; convolutional neural networks; CNN; classification; detection.

DOI: 10.1504/IJSAMI.2024.135403

International Journal of Sustainable Agricultural Management and Informatics, 2024 Vol.10 No.1, pp.92 - 119

Received: 03 Jan 2023
Accepted: 23 Mar 2023
Published online: 08 Dec 2023 *

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